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arxiv 2402.04717 v1 pith:VYEQI47W submitted 2024-02-07 cs.CV

InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior

classification cs.CV
keywords scenesynthesisgraphinstructscenepriorsemanticcontrollabilitydistributions
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Comprehending natural language instructions is a charming property for 3D indoor scene synthesis systems. Existing methods directly model object joint distributions and express object relations implicitly within a scene, thereby hindering the controllability of generation. We introduce InstructScene, a novel generative framework that integrates a semantic graph prior and a layout decoder to improve controllability and fidelity for 3D scene synthesis. The proposed semantic graph prior jointly learns scene appearances and layout distributions, exhibiting versatility across various downstream tasks in a zero-shot manner. To facilitate the benchmarking for text-driven 3D scene synthesis, we curate a high-quality dataset of scene-instruction pairs with large language and multimodal models. Extensive experimental results reveal that the proposed method surpasses existing state-of-the-art approaches by a large margin. Thorough ablation studies confirm the efficacy of crucial design components. Project page: https://chenguolin.github.io/projects/InstructScene.

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Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

    cs.AI 2026-05 unverdicted novelty 7.0

    SceneCode compiles natural language prompts into executable code programs that generate editable, articulated indoor scenes for physics simulation.

  2. Graph-PiT: Enhancing Structural Coherence in Part-Based Image Synthesis via Graph Priors

    cs.CV 2026-04 unverdicted novelty 7.0

    Graph-PiT adds graph priors and a hierarchical GNN to part-based image synthesis to enforce relational constraints and improve structural coherence over vanilla PiT.

  3. SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion

    cs.CV 2026-07 conditional novelty 6.0

    SynCity 3000 generates large, coherent 3D scenes from text by fine-tuning an image-to-3D diffusion model to operate convolutionally on overlapping windows, trained on procedurally generated synthetic scene data.

  4. VoxScene: Anchor-Conditioned Voxel Diffusion for Indoor Scene Arrangement

    cs.GR 2026-05 unverdicted novelty 6.0

    VoxScene is a new anchor-conditioned voxel diffusion model that synthesizes collision-free 3D indoor scene arrangements via discrete volumetric occupancies and uses the grids for asset retrieval.

  5. STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics-Physics Dual System

    cs.CV 2026-05 unverdicted novelty 6.0

    STABLE generates simulation-ready tabletop scenes by alternating a semantic LLM reasoner for task-aligned coarse layouts with a physics corrector for physical plausibility using progressive scene expansion.

  6. HetScene: Heterogeneity-Aware Diffusion for Dense Indoor Scene Generation

    cs.CV 2026-05 unverdicted novelty 6.0

    HetScene proposes a two-stage heterogeneous diffusion framework that decomposes scenes into primary structural objects and secondary contextual objects to generate denser, more plausible indoor layouts.

  7. PhyMix: Towards Physically Consistent Single-Image 3D Indoor Scene Generation with Implicit--Explicit Optimization

    cs.CV 2026-04 unverdicted novelty 6.0

    PhyMix unifies a new multi-aspect physics evaluator with implicit policy optimization and explicit test-time correction to produce single-image 3D indoor scenes that are both visually faithful and physically plausible.

  8. Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models

    cs.CV 2025-11 unverdicted novelty 6.0

    A feed-forward video latent transformer that predicts time-varying 3D Gaussian primitives from one image to produce controllable 4D scenes with appearance, geometry, and motion.

  9. Native3D: End-to-End 3D Scene Generation via Unified Mesh-Texture Modeling and Semantic Alignment

    cs.CV 2026-06 unverdicted novelty 5.0

    Native3D introduces a direct 3D scene generation method using unified mesh-texture representation and 3D REPA Loss for semantic alignment, claimed to outperform prior 2D-dependent approaches.

  10. DecoRec: Decomposed 3D Scene Reconstruction from Single-View Images via Object-Level Diffusion

    cs.CV 2026-05 unverdicted novelty 5.0

    DecoRec decomposes single-view 3D scene reconstruction into per-object diffusion reconstructions followed by a differentiable rendering and diffusion-guided merging pipeline.

  11. HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models

    cs.CV 2026-04 unverdicted novelty 5.0

    HOG-Layout enables text-driven hierarchical 3D scene generation, optimization, and real-time editing using LLMs, VLMs, RAG for semantic consistency, and an optimization module for physical plausibility.

  12. RoomPilot: Controllable Indoor Scene Synthesis via Multimodal Semantic Parsing

    cs.CV 2025-12 unverdicted novelty 5.0

    RoomPilot introduces a multimodal framework that maps text and floor plans to an Indoor Domain-Specific Language and uses a hierarchical pipeline for controllable indoor scene synthesis.

  13. Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments

    cs.AI 2026-07 unverdicted novelty 3.0

    SPG-Layout combines statistical object priors with hierarchical large-object-first placement to produce physically plausible text-driven 3D scenes in non-Manhattan rooms and outperforms baselines on a new 500-scene benchmark.